Name
Abstract | ||
|---|---|---|
We present a methodology for the extraction of narrative information from a large corpus. The key idea is to transform the corpus into a network, formed by linking the key actors and objects of the narration, and then to analyse this network to extract information about their relations. By representing information into a single network it is possible to infer relations between these entities, including when they have never been mentioned together. We discuss various types of information that can be extracted by our method, various ways to validate the information extracted and two different application scenarios. Our methodology is very scalable, and addresses specific research needs in social sciences. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1017/S1351324913000247 | NATURAL LANGUAGE ENGINEERING |
Field | DocType | Volume |
Computer science,Narrative,Artificial intelligence,Natural language processing,Network analysis,Scalability | Journal | 21 |
Issue | ISSN | Citations |
1.0 | 1351-3249 | 0 |
PageRank | References | Authors |
0.34 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saatviga Sudhahar | 1 | 7 | 2.67 |
| Gianluca de Fazio | 2 | 0 | 0.34 |
| Roberto Franzosi | 3 | 1 | 1.03 |
| Nello Cristianini | 4 | 11074 | 1239.05 |